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Robust and Efficient Fitting of Loss Models

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  • Vytaras Brazauskas

Abstract

We consider robust and efficient fitting of claim severity models whose parameters are estimated using the method of trimmed moments, which was recently introduced by Brazauskas, Jones, and Zitikis. In this article we take the “next” step by going beyond the theory and simulations. We address important issues that arise in practical application of the method. Specifically, we introduce two graphical diagnostic tools that can be used to choose the trimming proportions and hence help one to decide on the appropriate trade-off between robustness and efficiency. What is equally important, such tools are useful in model selection, for assessing the overall goodness of model fit, and for identification of outliers. Some insights about the choice between a “good” fit and an “even better” fit and its impact on risk evaluations are provided. Data analysis and illustrations are performed using real data that represent the total damage done by 827 fires in Norway for the year 1988.

Suggested Citation

  • Vytaras Brazauskas, 2009. "Robust and Efficient Fitting of Loss Models," North American Actuarial Journal, Taylor & Francis Journals, vol. 13(3), pages 356-369.
  • Handle: RePEc:taf:uaajxx:v:13:y:2009:i:3:p:356-369
    DOI: 10.1080/10920277.2009.10597561
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    Cited by:

    1. Fung, Tsz Chai, 2022. "Maximum weighted likelihood estimator for robust heavy-tail modelling of finite mixture models," Insurance: Mathematics and Economics, Elsevier, vol. 107(C), pages 180-198.
    2. Nadezhda Gribkova & Ričardas Zitikis, 2019. "Weighted allocations, their concomitant-based estimators, and asymptotics," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 71(4), pages 811-835, August.
    3. Brazauskas, Vytaras & Kleefeld, Andreas, 2009. "Robust and efficient fitting of the generalized Pareto distribution with actuarial applications in view," Insurance: Mathematics and Economics, Elsevier, vol. 45(3), pages 424-435, December.
    4. Kris Peremans & Stefan Van Aelst & Tim Verdonck, 2018. "A Robust General Multivariate Chain Ladder Method," Risks, MDPI, vol. 6(4), pages 1-18, September.

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